Metadata-Version: 2.1
Name: pveagle
Version: 0.2.0
Summary: Eagle Speaker Recognition Engine
Home-page: https://github.com/Picovoice/eagle
Author: Picovoice
Author-email: hello@picovoice.ai
License: UNKNOWN
Description: # Eagle Speaker Recognition Engine
        
        Made in Vancouver, Canada by [Picovoice](https://picovoice.ai)
        
        Eagle is an on-device speaker recognition engine. Eagle is:
        
        - Private; All voice processing runs locally.
        - Cross-Platform:
            - Linux (x86_64), macOS (x86_64, arm64), Windows (x86_64)
            - Android and iOS
            - Chrome, Safari, Firefox, and Edge
            - Raspberry Pi (4, 3) and NVIDIA Jetson Nano
        
        ## Compatibility
        
        - Python 3.5 or higher
        - Runs on Linux (x86_64), macOS (x86_64, arm64), Windows (x86_64), Raspberry Pi (4, 3), and NVIDIA Jetson Nano.
        
        ## Installation
        
        ```console
        pip3 install pveagle
        ```
        
        ## AccessKey
        
        Eagle requires a valid Picovoice `AccessKey` at initialization. `AccessKey` acts as your credentials when using Eagle
        SDKs. You can get your `AccessKey` for free. Make sure to keep your `AccessKey` secret.
        Signup or Login to [Picovoice Console](https://console.picovoice.ai/) to get your `AccessKey`.
        
        ## Usage
        
        Eagle has two distinct steps: Enrollment and Recognition. In the enrollment step, Eagle analyzes a series of
        utterances from a particular speaker to learn their unique voiceprint. This step produces an `EagleProfile` object,
        which can be stored and utilized during inference. During the Recognition step, Eagle compares the incoming frames of
        audio to the voiceprints of all enrolled speakers in real-time to determine the similarity between them.
        
        ### Speaker Enrollment
        
        Create an instance of the profiler:
        
        ```python
        import pveagle
        
        # AccessKey obtained from Picovoice Console (https://console.picovoice.ai/)
        access_key = "${ACCESS_KEY}"
        eagle_profiler = pveagle.create_profiler(access_key)
        ```
        
        `EagleProfiler` is responsible for processing and enrolling PCM audio data, with the valid audio sample rate determined
        by `eagle_profiler.sample_rate`. The audio data must be 16-bit linearly-encoded and single-channel.
        
        When passing samples to `eagle_profiler.enroll`, the number of samples must be at
        least `eagle_profiler.min_enroll_samples` to ensure sufficient data for enrollment. The percentage value
        obtained from this process indicates the progress of enrollment, while the feedback value can be utilized to determine
        the status of the enrollment process.
        
        ```python
        def get_next_enroll_audio_data(num_samples):
            pass
        
        
        percentage = 0.0
        while percentage < 100.0:
            percentage, feedback = eagle_profiler.enroll(get_next_enroll_audio_data(eagle_profiler.min_enroll_samples))
            print(feedback.name)
        ```
        
        After the percentage reaches 100%, the enrollment process is considered complete. While it is possible to continue
        providing additional audio data to the profiler to improve the accuracy of the voiceprint, it is not necessary to do so.
        Moreover, if the audio data submitted is unsuitable for enrollment, the feedback value will indicate the reason, and the
        enrollment progress will remain unchanged.
        
        ```python
        speaker_profile = eagle_profiler.export()
        ```
        
        The `eagle_profiler.export()` function produces an `EagleProfile` object, which can be converted into a binary form
        using the `EagleProfile.to_bytes()` method. This binary representation can be saved and subsequently retrieved using
        the `EagleProfile.from_bytes()` method.
        
        To reset the profiler and enroll a new speaker, the `eagle_profiler.reset()` method can be used. This method clears all
        previously stored data, making it possible to start a new enrollment session with a different speaker.
        
        Finally, when done be sure to explicitly release the resources:
        
        ```python
        eagle_profiler.delete()
        ```
        
        ### Speaker Recognition
        
        Create an instance of the engine with one or more speaker profiles from the `EagleProfiler`:
        
        ```python
        eagle = pveagle.create_recognizer(access_key, speaker_profile)
        ```
        
        When initialized, `eagle.sample_rate` specifies the valid sample rate for Eagle. The expected length of a frame, or the
        number of audio samples in an input array, is defined by `eagle.frame_length`.
        
        Like the profiler, Eagle is designed to work with single-channel audio that is encoded using 16-bit linear PCM.
        
        ```python
        def get_next_audio_frame():
            pass
        
        
        while True:
            scores = eagle.process(get_next_audio_frame())
        ```
        
        The `scores` array contains floating-point numbers that indicate the similarity between the input audio frame and the
        enrolled speakers. Each value in the array corresponds to a specific enrolled speaker, maintaining the same order as the
        speaker profiles provided during initialization. The values in the array range from 0.0 to 1.0, where higher values
        indicate a stronger degree of similarity.
        
        Finally, when done be sure to explicitly release the resources:
        
        ```python
        eagle.delete()
        ```
        
        ## Demos
        [pveagledemo](https://pypi.org/project/pveagledemo/) provides command-line utilities for processing real-time
        audio (i.e. microphone) and files using Eagle.
        
Keywords: Speaker Recognition,Speaker Identification,Voice Recognition,Voice Identification
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Multimedia :: Sound/Audio :: Speech
Requires-Python: >=3.5
Description-Content-Type: text/markdown
